Objective. Technical advances in deep brain stimulation (DBS) are crucial to improve therapeutic efficacy and battery life. We report the potentialities and pitfalls of one of the first... Show moreObjective. Technical advances in deep brain stimulation (DBS) are crucial to improve therapeutic efficacy and battery life. We report the potentialities and pitfalls of one of the first commercially available devices capable of recording brain local field potentials (LFPs) from the implanted DBS leads, chronically and during stimulation. The aim was to provide clinicians with well-grounded tips on how to maximize the capabilities of this novel device, both in everyday practice and for research purposes. Approach. We collected clinical and neurophysiological data of the first 20 patients (14 with Parkinson's disease (PD), five with dystonia, one with chronic pain) that received the Percept (TM) PC in our centres. We also performed tests in a saline bath to validate the recordings quality. Main results. The Percept PC reliably recorded the LFP of the implanted site, wirelessly and in real time. We recorded the most promising clinically useful biomarkers for PD and dystonia (beta and theta oscillations) with and without stimulation. Furthermore, we provide an open-source code to facilitate export and analysis of data. Critical aspects of the system are presently related to contact selection, artefact detection, data loss, and synchronization with other devices. Significance. New technologies will soon allow closed-loop neuromodulation therapies, capable of adapting stimulation based on real-time symptom-specific and task-dependent input signals. However, technical aspects need to be considered to ensure reliable recordings. The critical use by a growing number of DBS experts will alert new users about the currently observed shortcomings and inform on how to overcome them. Show less
Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD)... Show moreStudying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers. Show less
